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171 results for “Control region”
iNRACM: Incorporating 15N into the Regional Atmospheric Chemistry Mechanism (RACM) for assessing the role photochemistry plays in controlling the isotopic composition of NOx, NOy, and atmospheric nitrate
<p><sup>15</sup>N compounds and reactions were incorporated into Regional Atmospheric Chemistry Mechanism (RACM), based on recent experimental or calculated values of isotope fractionation factors (α), to simulate δ<sup>15</sup>N values in NO<sub>y</sub> compounds.</p>
Supplement A. Wolf et al: 'Western Caucasus regional hydroclimate controlled by cold-season temperature variability since the Last Glacial Maximum'
<p>This repository contains all proxy data presented in A. Wolf et al, "Western Caucasus regional hydroclimate controlled by cold-season temperature variability since the Last Glacial Maximum". The data can be used to replicate figures and analyses presented in the main text. Additionally, data can be accessed in the supplement material and in the data availability statement. </p>
Figure 2 in Examining metrics and magnitudes of molecular genetic differentiation used to delimit cetacean subspecies based on mitochondrial DNA control region sequences
Figure 2. Relationship between ΦST and Nei's estimate of net divergence (dA) among cetacean population, subspecies, and species pairs estimated using mitochondrial DNA control region sequence data. Specific values mentioned in the text are numbered: 1 = Neophocaena species; 2 = killer whale populations. The three green squares in the left-hand side of the figure (ΦST <0.07) represent, from bottom to top, the subspecies comparisons for S. attenuata, S. longirostris, and L. obscurus, respectively.
Figure 1 in Examining metrics and magnitudes of molecular genetic differentiation used to delimit cetacean subspecies based on mitochondrial DNA control region sequences
Figure 1. Box and whisker plots showing median and 1st and 3rd quartiles, and minimum and maximum values for six metrics of genetic divergence among cetacean population, subspecies, and species pairs estimated using mitochondrial DNA control region sequence data.
Figure 7 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 7. Median-joining network of 68 haplotypes from 485 wild boars from different regions of the world. Circle sizes are proportional to haplotype frequencies and numbers refer to haplotype codes in Table 2. Numbers on the branches indicate the number of nucleotide substitutions, if more than one. Major haplogroups are delimited by dashed lines. A, Asian; NE, Near Eastern; E1, European E1; E2, European E2. Haplotypes exclusive to Turkey are labeled in green.
Figure 3 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 3. Maximum likelihood phylogenetic tree of haplotypes of the Turkish wild boars (Sus scrofa) obtained in the present study, based on the partial D-loop sequences of mtDNA. Numbers above or below branches indicate bootstrap values. TR numbers refer to current haplotype numbers in Table 1 and H numbers refer to the published haplotype labels downloaded from GenBank (Table 2). E1: European 1 haplogroup/clade in Figure 5, NE: Near East haplogroup/ clade in Figure 5.
Figure 2 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 2. Various phenotypes of obtained Turkish wild boar individuals from different localities studied presently.
Figure 6 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 6. Bayesian inference tree based on 68 partial D-loop haplotypes of 485 wild boars (both obtained in this study and downloaded from GenBank). Posterior probabilities are indicated at nodes. Haplogroups: A, Asian; NE, Near Eastern; E1, European E1; E2, European E2. Outgroup taxa are Sus barbatus and Phacochoerus aethiopicus.
Figure 1 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 1. Location map of the 70 samples examined in this study: black dots indicate local samples of one or more individuals and numbers refer to locality names in Table 1. TT (Turkish Thrace, 1–4), SWA (Southwestern Anatolia, 5–11), CA (Central Anatolia, 12–26), NEA (Northeastern Anatolia, 27–30), and SEA (Southeastern Anatolia, 31–34).
Figure 5 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 5. Maximum likelihood tree based on 68 haplotypes from 485 wild boar D-loop region sequences (both obtained in this study and downloaded from GenBank). Numbers above or below branches indicate bootstrap support values. Haplogroups: A, Asian; NE, Near Eastern; E1, European E1; E2, European E2. Outgroup taxa are Sus barbatus and Phacochoerus aethiopicus.
Figure 4 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 4. Bayesian phylogenetic tree constructed from haplotypes of the wild boar (Sus scrofa) samples collected in this study, based on the partial D-loop sequences of mtDNA. Posterior probabilities are indicated at nodes. TR numbers refer to current haplotype numbers in Table 1 and H numbers refer to the published haplotype labels downloaded from GenBank (Table 2). E1: European 1 haplogroup/clade in Figure 5, NE: Near East haplogroup/clade in Figure 5.
Evaluation of comparative efficacy of Trayodashang Guggul versus Asitaka Churna Vati in the management of Katigraha (Stiffness in Lumbar Region): A randomized controlled trial protocol
Open the record for dataset details and reuse information.
Eocene to Oligocene vegetation and climate in the Tasmanian Gateway region controlled by changes in ocean currents and pCO2
<p>Datasets accompanying Eocene to Oligocene vegetation and climate in the Tasmanian Gateway region controlled by changes in ocean currents and pCO2 by Amoo et al.</p> <p>Supplementary table S1: Raw palynomorph assemblage data, total counts, ODP Site 1172 </p> <p>Supplementary table S2: Sporomorph-based climate estimates including MAT, WMMT, CMMT and MAP </p> <p>Supplementary table S3: Sporomorph diversity indices</p>
Effectiveness of seasonal malaria chemoprevention administered in a mass campaign in the Kedougou region of Senegal in 2016: a Case-control study
<p><strong><span>Context</span></strong></p> <p><span>Seasonal malaria chemoprevention (SMC) with Sulfadoxine-Pyrimethamine plus Amodiaquine (SPAQ) is a malaria prevention strategy recommended since 2012 by the World Health Organization (WHO) for children under 5 years of age. In Senegal, the scaling up of the SMC has started since 2013 in the south-eastern regions of the country with an extension of the target to 10 years old children. The scaling up of SMC requires a regular evaluation of the strategy as recommended by the WHO. This study was conducted to evaluate the effectiveness of SMC. </span></p> <p><strong><span>Methodology </span></strong></p> <p><span>A case-control study was conducted in some villages of the health districts of Saraya and Kedougou in the Kedougou region from July to December 2016. A "case" was a sick child, aged 3 months to 10 years, seen in consultation and with a positive RDT. The "control" was a child of the same age group with a negative RDT and living in the same compound as the case or in a neighbouring compound. Each case was matched with two controls. Exposure to SMC was assessed by interviewing the mothers/caretakers and by checking the SMC administration card.</span></p> <p><strong><span>Results</span></strong></p> <p><span>A total of 492 children, including 164 cases and 328 controls, were recruited for our study. Their mean ages were 5.32 (+/- 2.15) and 4.44 (+/-2.25) years for the cases and the controls respectively. Male children predominated in both cases (55.49%) and controls (51.22%) (p=</span><span>0.18)</span><span>. Net ownership was 85.80% among cases and 90.85% among controls (p=0.053). The proportion of controls who received SMC was higher than that of cases (98.17% vs 85.98%; (p=1.10<sup>-7</sup>)). The protective effectiveness of SMC was 89% </span><span>(IC 95% = 84–93%)</span><span> </span><span>(OR=0.11).</span></p> <p><strong><span>Conclusion </span></strong></p> <p><span>SMC is therefore an effective strategy in the control of malaria in children. Case-control studies are a good approach for monitoring the efficacy of drugs administered during SMC.</span></p>
Regional Patterns and Physical Controls of Streamflow Generation across the Conterminous United States
<p>This dataset contains information of 432 study catchments used in the WRR paper "Regional Patterns and Physical Controls of Streamflow Generation across the Conterminous United States", including shapefile, hydrological signatures, catchment descriptors, and catchment classification (the primary class of each catchment).</p>
NMR metabolomic analysis of Drosophila extracts: 3 genotypes (control, RNAi Opa1 in muscle, RNAi Marf in muscle), 3 body regions (head, thorax, abdomen), 2 ages (30 days, 65 days)
<p>RNAi of the Drosophila mitochondrial fusion genes Opa1 and Marf (Mitofusin 2) in the muscle results in an extended lifespan. In order to unravel the metabolic changes in the long-living flies we analysed the metabolome by NMR spectroscopy, comparing the two knock-down genotypes to a wild type control at two ages: 30 days (young flies) and 65 days (old flies). In order to detect autonomous and non-autonomous changes, we also divided the samples in three anatomical regions: head (mostly neural tissue) thorax (mostly muscle), and abdomen (visceral, reproductive, metabolic control)</p>
Regional controls on the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)
<p>Data corresponding to "Regional controls of the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)" (Bischof et al., in prep.).</p> <p>model_setup contains the input and code directories needed to run our model using MITgcm. data contains all model output used to plot the figures contained in the paper.</p> <p> </p>
Oligocene–Miocene exhumation of the Pinaleño metamorphic core complex, southeastern Arizona: support for magmatism and plate margin reorganization as controls on regional exhumation trends
<p>We present thermochronology data from igneous and metamorphic rock samples collected from the Pinale<strong>ñ</strong>o Mountains, southeast Arizona, U.S.A. Our interpretations of the data are described and discussed in a paper in the journal Tectonics, titled “Oligocene–Miocene exhumation of the Pinaleño metamorphic core complex, southeastern Arizona: support for magmatism and plate margin reorganization as controls on regional exhumation trends.” New data include biotite and white mica Ar/Ar analyses, apatite fission track analyses, apatite (U-Th-Sm)/He analyses, and zircon (U-Th-Sm)/He analyses. The data were used for inverse HeFTy modeling and forward radiation damage and annealing modeling (RDAAM) to investigate time-temperature histories. The data set comprises a table summarizing the results of Ar/Ar analyses (Table S1), a table with the detailed results of zircon (U-Th-Sm)/He thermochronology (Table S2), a table with the detailed results of apatite (U-Th-Sm)/He thermochronology (Table S3), a table summarizing the results of apatite fission track analyses, including radial plots (Table S4), and a table summarizing the input data and constraints used for inverse modeling.</p>
Scripts and Data for "Disentangling the Hydrological and Hydraulic Controls on Streamflow Variability in E3SM V2 – A Case Study in the Pantanal Region"
<p>Matlab scripts for processing and showing the coupled ELM-MOSART coupled simulations for Pantanal region.</p> <p>domain_lnd_Pantanal_default.nc, MOSART_Pantanal_default_c211116.nc, and surfdata_Pantanal_default_c220520.nc are the domain file, MOSART input file, and ELM surface dataset, respectively. </p> <p><a href="https://zenodo.org/api/files/6be468b6-cbfa-4ef4-bb10-0f15812be9bd/Pantanal_half_calibration_CLMCRUNCEPv7.sh">Pantanal_half_calibration_CLMCRUNCEPv7.sh</a> is the bash script to run E3SM with coupled ELM-MOSART configuration. ANd detailed instruction of running E3SMV2 can be found at: https://e3sm.org/model/running-e3sm/e3sm-quick-start/ (last access: Aug 2023).</p> <p>Pantanal_GSIM.zip contains the observed streamflow that used in this study, which is downloaded from <a href="https://doi.pangaea.de/10.1594/PANGAEA.887470">https://doi.pangaea.de/10.1594/PANGAEA.887470</a> (last access: Aug 2023). The reference is Gudmundsson, Lukas; Do, Hong Xuan; Leonard, Michael; Westra, Seth (2018): The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment. Earth System Science Data, 10(2), 787-804, https://doi.org/10.5194/essd-10-787-2018.</p> <p>BFI3.mat is the baseflow index from GSCD and processed to the study domain. The GSCD dataset was download from <a href="http://www.gloh2o.org/gscd/">http://www.gloh2o.org/gscd/</a> (last access: Aug 2023). The reference is Beck, H. E., van Dijk, A. I. J. M., Miralles, D. G., de Jeu, R. A. M., Bruijnzeel, L. A., McVicar, T. R., and Schellekens, J.: Global patterns in base flow index and recession based on streamflow observations from 3394 catchments, Water Resour Res, 49, 7843-7863, <a href="https://doi.org/10.1002/2013WR013918">https://doi.org/10.1002/2013WR013918</a>, 2013.</p> <p>runoff_uncertianty.mat contains the annual runoff time series from GRUN, LORA, and GFRF that processed to the study domain. The GRUN runoff dataset was downloaded from <a href="https://doi.org/10.6084/m9.figshare.9228176">https://doi.org/10.6084/m9.figshare.9228176</a> (last access: Aug 2023). The LORA runoff dataset was downloaded from <a href="https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml">https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml</a> (last access: Aug 2023). The reference is Hobeichi, S., Abramowitz, G., Evans, J., and Beck, H. E.: Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product, Hydrol. Earth Syst. Sci., 23, 851-870, 10.5194/hess-23-851-2019, 2019. The GRFR runoff was downloaded from <a href="http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/">http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/</a> (last access: Aug 2023). The reference is Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. d. H., Lu, H., Yang, K., Hong, Y., and Wood, E. F.: Global Reach-level 3-hourly River Flood Reanalysis (1980-2019), B Am Meteorol Soc, 1-49, 10.1175/BAMS-D-20-0057.1, 2021.</p> <p>GLAD_Pantanal_half.mat, GLAD_Pantanal_8th.mat are the processed surface water fraction from GLAD at half and 8th spatial resoution. Specifically, The GLAD surface water dynamics was downloaded from <a href="https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects">https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects</a> (last access: Aug 2023). The reference is Pickens, A. H., Hansen, M. C., Hancher, M., Stehman, S. V., Tyukavina, A., Potapov, P., Marroquin, B., and Sherani, Z.: Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sens Environ, 243, 111792, <a href="https://doi.org/10.1016/j.rse.2020.111792">https://doi.org/10.1016/j.rse.2020.111792</a>, 2020.</p>
A Multi-site Randomized Controlled Trial Comparing Regional and General Anesthesia for Effects on Neurodevelopmental Outcome and Apnea in Infants
ClinicalTrials.gov study NCT00756600. IPD Sharing: Not stated. Countries: 7. Publications: 3.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.